Relay communication method and apparatus based on spectrum sharing network, relay, and medium

Abstract

The embodiment of the application discloses a kind of based on spectrum sharing network's relay communication method and device, relay and storage medium, wherein the spectrum sharing network includes: the primary network of satellite composition, and the secondary network of one base station, one secondary user and at least two relays are constituted;The method is applied to the relay, and the method comprises: receiving the useful signal and the interference signal that the satellite transmits in the first time slot in the first time slot that the base station transmits;By the weight value of beam forming that is determined in advance, the first signal received by all the relay in the first time slot is weighted and added, to obtain second signal;Wherein, the first signal is the sum of the useful signal and the interference signal;Second signal is sent to the secondary user in the second time slot.

Description

Technical Field

[0001] This application relates to communication technology, including but not limited to a relay communication method and apparatus, relay and medium based on a spectrum sharing network. Background Technology

[0002] In recent years, the construction of communication infrastructure has achieved unprecedented and tremendous progress. Although the modern communication network, mainly composed of wired and wireless communication, has reached a considerable scale, the relative insufficiency of broadband wireless access resources remains one of the "bottlenecks" hindering the development of informatization. To fundamentally solve this problem, in addition to fully exploring the potential of existing communication resources, it is also necessary to actively explore new communication methods that are complementary and compatible with already commercialized communication systems and can promptly apply the latest research results.

[0003] Currently, while traditional cellular wireless networks can indeed provide stable and reliable communication quality to a certain extent, they cannot meet the needs of some fast-moving and urgent communication applications. Thus, drone communication has emerged, and due to the excellent autonomy, flexibility, and hovering capabilities of drones, they have gained widespread attention in recent years.

[0004] However, current technologies only consider the application of single drones in mobile communication networks. Single drones are limited by their limited energy supply, which restricts their flight distance. At the same time, single drone networks are also vulnerable to various network attacks such as Trojans. These factors lead to the disadvantages of single drone communication, such as weak resilience, limited coverage, and unstable links. Summary of the Invention

[0005] In view of this, embodiments of this application provide a relay communication method and apparatus, relay and medium based on a spectrum sharing network.

[0006] The technical solution of this application embodiment is implemented as follows:

[0007] In a first aspect, embodiments of this application provide a relay communication method based on a spectrum-sharing network, wherein the spectrum-sharing network includes: a main network composed of satellites, and a secondary network composed of a base station, a secondary user, and at least two relays; the method is applied to the relays, and the method includes:

[0008] The useful signal transmitted by the base station and the interference signal transmitted by the satellite are received in the first time slot;

[0009] The first signal received by all the relays in the first time slot is weighted and summed using pre-determined beamforming weights to obtain the second signal; wherein the first signal is the sum of the useful signal and the interference signal;

[0010] The second signal is sent to the secondary user in the second time slot.

[0011] Secondly, embodiments of this application provide a relay communication device based on a spectrum sharing network, wherein the spectrum sharing network includes: a main network composed of satellites, and a secondary network composed of a base station, a secondary user, and at least two relays;

[0012] The device includes:

[0013] A receiving unit is configured to receive a useful signal transmitted by the base station and an interference signal transmitted by the satellite in a first time slot;

[0014] A weighting unit is used to weight and sum all the first signals received by the relays in the first time slot using predetermined beamforming weights to obtain a second signal; wherein the first signal is the sum of the useful signal and the interference signal;

[0015] A transmitting unit is used to transmit the second signal to the secondary user in the second time slot.

[0016] Thirdly, embodiments of this application provide a relay, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the program, it implements the steps in the relay communication method based on the spectrum sharing network described above.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the relay communication method based on a spectrum sharing network described above.

[0018] This application provides a relay communication method and apparatus, relay, and medium based on a spectrum sharing network. The method involves receiving a useful signal transmitted by a base station and an interference signal transmitted by a satellite in a first time slot; weighting and summing the first signals received by all relays in the first time slot using pre-determined beamforming weights to obtain a second signal; wherein the first signal is the sum of the useful signal and the interference signal; and transmitting the second signal to the secondary user in a second time slot. This enables network communication based on multiple relays, effectively expanding the coverage of the relay network, improving system capacity and spectrum utilization. Furthermore, since the signal sent to the relay user is a beamformed signal, the communication quality of the relay user is improved. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the implementation process of the relay communication method in the embodiments of this application. Figure 1 ;

[0020] Figure 2 This is a schematic diagram of the implementation process of the relay communication method in the embodiments of this application. Figure 2 ;

[0021] Figure 3 This is a schematic diagram of the implementation process of the relay communication method in the embodiments of this application. Figure 3 ;

[0022] Figure 4A This is a schematic diagram of the composition structure of the spectrum sharing network in an embodiment of this application;

[0023] Figure 4B This is a simulation diagram of a relay communication method based on a spectrum sharing network according to an embodiment of this application;

[0024] Figure 5 This is a schematic diagram of the composition structure of the relay communication device in the embodiments of this application;

[0025] Figure 6 This is a schematic diagram of a hardware entity in an embodiment of this application. Detailed Implementation

[0026] The technical solutions of this application will be further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0027] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0028] In the following description, the use of suffixes such as "module," "part," or "unit" to denote elements is solely for the purpose of illustration and has no specific meaning in itself. Therefore, "module," "part," or "unit" may be used interchangeably.

[0029] It should be noted that the terms "first, second, and third" used in the embodiments of this application are merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0030] This application provides a relay communication method based on a spectrum sharing network. The method is applied to a relay, and the functions implemented by the method can be achieved by the processor in the relay calling program code. Of course, the program code can be stored in the storage medium of the relay.

[0031] The spectrum sharing network includes: a main network consisting of satellites, and a secondary network consisting of a base station, a secondary user, and at least two relays; Figure 1 This is a schematic diagram of the implementation process of the relay communication method in the embodiments of this application. Figure 1 ,like Figure 1 As shown, the method includes:

[0032] Step S101: Receive the useful signal transmitted by the base station and the interference signal transmitted by the satellite in the first time slot;

[0033] In this embodiment, the spectrum-sharing network consists of a satellite-to-ground network as the primary network and a relay network as a secondary network, with the satellite-to-ground network and the relay network sharing the spectrum. The satellite-to-ground network includes satellites and one or more primary users that receive signals transmitted by the satellites. For example, the primary users can be VSAT (Very Small Aperture Terminal), UE (User Equipment), and base stations. The relay network includes one base station, one secondary user, and multiple relays. For example, the relays can be drones.

[0034] Here, in the spectrum-sharing network, the relay communication process can be divided into two time slots: In the first time slot, the base station sends a useful signal to the relay, while the relay simultaneously receives interference signals from the satellite. In the second time slot, the signals received by each of the multiple relays (including useful signals, interference signals, and noise signals) are weighted and summed, and the weighted summed signal is forwarded to the secondary user. Of course, in the first time slot, the primary user also receives the useful signal from the satellite. Furthermore, due to the broadcast characteristics of mobile communication, the primary user is also subject to interference from the signals transmitted by the relays. Therefore, the primary user receives both the useful signal transmitted by the satellite in the second time slot and the interference signals transmitted by the relays.

[0035] Step S102: The first signals received by all the relays in the first time slot are weighted and summed using pre-determined beamforming weights to obtain a second signal; wherein the first signal is the sum of the useful signal and the interference signal;

[0036] Here, beamforming is a combination of antenna technology and digital signal processing technology, used for directional signal transmission or reception. Beamforming is a classic antenna technique originating from a concept in adaptive antennas. Signal processing at the transmitting end involves weighted combining of the signals transmitted from multiple antenna elements to form the desired ideal signal, which is then sent to the receiving end. From the perspective of the antenna pattern, this is equivalent to forming a beam pointing in a specific direction.

[0037] As can be seen, beamforming technology requires a multi-antenna system. The multiple relays in the spectrum sharing network of this application can be considered as a multi-antenna system. By using multiple sets of antennas (i.e., multiple relays), the desired ideal signal is formed, which can significantly improve the signal-to-noise ratio at the receiver and achieve good signal quality even when the receiver is far away.

[0038] Step S103: Send the second signal to the secondary user in the second time slot.

[0039] In this embodiment, the useful signal transmitted by the base station and the interference signal transmitted by the satellite are received in the first time slot; the first signal received by all the relays in the first time slot is weighted and summed using a pre-determined beamforming weight to obtain a second signal; wherein the first signal is the sum of the useful signal and the interference signal; the second signal is sent to the secondary user in the second time slot. In this way, network communication based on multiple relays can be carried out, effectively expanding the coverage of the relay network, improving system capacity and spectrum utilization. At the same time, since the signal sent to the relay user is a beamformed signal, the communication quality of the relay user is improved.

[0040] Based on the foregoing embodiments, this application further provides a relay communication method based on a spectrum sharing network, wherein the spectrum sharing network includes a primary network and a secondary network, wherein the primary network consists of a satellite and at least one primary user, and the secondary network consists of a base station, a secondary user, and at least two relays; Figure 2 This is a schematic diagram of the implementation process of the relay communication method in the embodiments of this application. Figure 2 ,like Figure 2 As shown, the method is applied to a relay, and the method includes:

[0041] Step S201: Receive the useful signal transmitted by the base station and the interference signal transmitted by the satellite in the first time slot;

[0042] Here, each of the relays receives the useful signal transmitted by the base station and the interference signal transmitted by the satellite in the first time slot.

[0043] Step S202: When the transmit power of each relay is lower than the maximum transmit power threshold and the signal-to-interference-plus-noise ratio (SIR) of each primary user is higher than the minimum SIR threshold, beamforming design is performed with the goal of maximizing the SIR of the secondary user to obtain the target optimization problem.

[0044] In this embodiment, the spectrum sharing network performs a weighted sum of the signals received by each of the multiple relays in the second time slot, and then forwards the weighted sum to the secondary user. Therefore, in order to obtain the weight vector (i.e., the beamforming weights) in the weighted summation operation, this embodiment uses maximizing the signal-to-interference-plus-noise ratio (SIR) of the secondary users of the secondary network as the objective function, and uses the constraints that the transmit power of each relay is lower than the maximum transmit power threshold and the SIR of the primary users of the primary network is higher than the minimum SIR threshold, to determine the optimization problem. The optimization variable of the optimization problem is the beamforming weight vector.

[0045] In this embodiment, the optimization problem is designed with the constraint that the transmission power of each of the multiple relays is lower than the maximum transmission power threshold. That is to say, unlike previous designs that used the total transmission power of the relays as a constraint, this embodiment considers the situation where the transmission power of each relay is limited, which has strong practical significance.

[0046] Step S203: Determine the beamforming weights based on the target optimization problem;

[0047] Here, steps S202 to S203 can also be performed by a third-party device other than the device in the spectrum sharing network, such as a network management device. Furthermore, steps S202 to S203 can be performed before or after step S201.

[0048] Step S204: The first signals received by all the relays in the first time slot are weighted and summed using the beamforming weights to obtain the second signal; wherein, the first signal is the sum of the useful signal and the interference signal;

[0049] Step S205: Send the second signal to the secondary user in the second time slot.

[0050] Here, by solving the target optimization problem and obtaining the optimal beamforming weight vector, the optimal beamforming weight vector can be used to weight and sum multiple first signals (each relay in the multiple relays will receive the first signal, so there are as many first signals as there are relays) to obtain the weighted second signal (i.e., the shaped beam). The shaped beam is then sent to the secondary user, which can solve the distributed beamforming problem in multi-relay networks and improve the received signal-to-interference-plus-noise ratio of secondary users while ensuring the communication quality of primary users and limiting the relay transmission power.

[0051] Based on the foregoing embodiments, this application further provides a relay communication method based on a spectrum sharing network. Figure 3 This is a schematic diagram of the implementation process of the relay communication method in the embodiments of this application. Figure 3 ,like Figure 3 As shown, the method is applied to a relay, and the method includes:

[0052] Step S301: Receive the useful signal transmitted by the base station and the interference signal transmitted by the satellite in the first time slot;

[0053] Step S302: When the transmit power of each relay is lower than the maximum transmit power threshold and the signal-to-interference-plus-noise ratio (SIR) of each primary user is higher than the minimum SIR threshold, beamforming design is performed with the goal of maximizing the SIR of the secondary user to obtain the target optimization problem.

[0054] Step S303: The target optimization problem is processed sequentially using the Charnes-Cooper transform method and the convexity method to obtain a target convex optimization problem; wherein, the target optimization problem is a non-convex optimization problem.

[0055] Here, if the objective optimization problem is a non-convex objective optimization problem, it can be transformed into a convex optimization problem, and then the existing convex optimization package can be used to solve the convex optimization problem.

[0056] Convex optimization, also known as convex optimization or convex minimization, is a subfield of mathematical optimization that studies the minimization of convex functions defined on convex sets. The convexity of convex functions allows powerful tools from convex analysis to be applied in optimization problems, such as second derivatives.

[0057] The convex optimization package is a toolkit for solving the convex optimization problem. For example, Cvxpy, Cvxopt, and SciPy are all convex optimization toolkits in Python, and there are also corresponding convex optimization toolkits in Matlab. Both Python and Matlab are computer programming languages.

[0058] Here, the convexity difference method, also known as the DC method, is an algorithm for finding the difference between two convex functions. In this embodiment, the Charnes-Cooper transform method and the DC method are used to transform the objective optimization problem from a non-convex optimization problem into a convex optimization problem.

[0059] Step S304: Solve the target convex optimization problem using an iterative algorithm to obtain the beamforming weights;

[0060] In this embodiment, by utilizing the Charnes-Cooper transform method and the DC method, the objective optimization problem is transformed from a non-convex optimization problem into a convex optimization problem. Therefore, the objective function in the transformed convex optimization problem is in the form of the difference between two convex functions. Furthermore, this embodiment employs an iterative algorithm to gradually approximate the optimal beamforming weights.

[0061] Step S305: The first signals received by all the relays in the first time slot are weighted and summed using the beamforming weights to obtain the second signal; wherein, the first signal is the sum of the useful signal and the interference signal;

[0062] Step S306: Send the second signal to the secondary user in the second time slot.

[0063] Based on the foregoing embodiments, this application further provides a relay communication method based on a spectrum sharing network. The method is applied to relay communication and includes:

[0064] Step S311: Receive the useful signal transmitted by the base station and the interference signal transmitted by the satellite in the first time slot;

[0065] Step S312: When the transmit power of each relay is lower than the maximum transmit power threshold and the signal-to-interference-plus-noise ratio (SIR) of each primary user is higher than the minimum SIR threshold, beamforming design is performed with the goal of maximizing the SIR of the secondary user, resulting in a target optimization problem; wherein the target optimization problem is a non-convex optimization problem.

[0066] Step S313: Transform the objective optimization problem into the first optimization problem of semidefinite programming;

[0067] Here, positive semidefinite programming is a generalization of linear programming. It is the problem of maximizing (minimizing) a linear function under the constraint that "the affine combination of symmetric matrices is positive semidefinite". This constraint is nonlinear, nonsmooth, and convex, thus positive semidefinite programming is a nonsmooth convex optimization problem.

[0068] Step S314: Process the first optimization problem using the Charnes-Cooper transform method to obtain the second optimization problem;

[0069] Step S315: Transform the objective function in the second optimization problem into the form of the difference between two convex functions to obtain the objective convex optimization problem;

[0070] Step S316: Solve the target convex optimization problem using an iterative algorithm to obtain the beamforming weights;

[0071] Step S317: The first signals received by all the relays in the first time slot are weighted and summed using the beamforming weights to obtain the second signal; wherein, the first signal is the sum of the useful signal and the interference signal;

[0072] Step S318: Send the second signal to the secondary user in the second time slot.

[0073] In some embodiments, step S316, solving the target convex optimization problem using an iterative algorithm to obtain the beamforming weights, can be achieved in the following ways:

[0074] Step S3161: Determine the iterative function based on the target convex optimization problem and the upper limit of the convex function;

[0075] Step S3162: Based on the iterative algorithm, solve the iterative function using the standard optimization toolkit;

[0076] Step S3163: Determine the weights of the beamforming based on the solution when the iterative function satisfies the convergence condition.

[0077] Based on the foregoing embodiments, this application further provides a relay communication method based on a spectrum sharing network. The method is applied to relay communication and includes:

[0078] Step S321: Receive the useful signal transmitted by the base station and the interference signal transmitted by the satellite in the first time slot;

[0079] Step S322: When the transmit power of each relay is lower than the maximum transmit power threshold and the signal-to-interference-plus-noise ratio (SIR) of each primary user is higher than the minimum SIR threshold, beamforming design is performed with the goal of maximizing the SIR of the secondary user, resulting in a target optimization problem; wherein the target optimization problem is a non-convex optimization problem.

[0080] Step S323: Add a constraint rank(W) = 1 to the target optimization problem to transform the target optimization problem into the first optimization problem; where W = ww Hw is the weight of the beamforming;

[0081] Here, H stands for conjugate transpose in mathematical notation.

[0082] Step S324: Process the first optimization problem using the Charnes-Cooper transform method to obtain the second optimization problem;

[0083] Step S325: Transform the objective function in the second optimization problem into the form of the difference between two convex functions to obtain the objective convex optimization problem;

[0084] Step S326: Solve the target convex optimization problem using an iterative algorithm to obtain the beamforming weights;

[0085] In this embodiment, the Charnes-Cooper transform and DC method are used to transform the objective optimization problem from a non-convex problem to a convex one. Therefore, the objective function in the transformed convex optimization problem is in the form of the difference between two convex functions. This difference can be viewed as a DC programming problem, and thus an approximation method is used to find the upper bound of the objective function. Therefore, this embodiment employs an iterative algorithm to gradually approximate the optimal beamforming weights.

[0086] Step S327: The first signals received by all the relays in the first time slot are weighted and summed using the beamforming weights to obtain the second signal; wherein the first signal is the sum of the useful signal and the interference signal;

[0087] Step S328: Send the second signal to the secondary user in the second time slot.

[0088] Based on the foregoing embodiments, this application further provides a relay communication method based on a spectrum sharing network. The method is applied to relay communication and includes:

[0089] Step S331: Receive the useful signal transmitted by the base station and the interference signal transmitted by the satellite in the first time slot;

[0090] Step S332: When the transmit power of each relay is lower than the maximum transmit power threshold and the signal-to-interference-plus-noise ratio (SIR) of each primary user is higher than the minimum SIR threshold, beamforming design is performed with the goal of maximizing the SIR of the secondary user, resulting in a target optimization problem; wherein the target optimization problem is a non-convex optimization problem.

[0091] Step S333: Add a constraint rank(W) = 1 to the objective optimization problem to transform it into a first optimization problem; where W = ww Hw is the weight of the beamforming;

[0092] Step S334: Process the first optimization problem using the Charnes-Cooper transform method, and convert tr(X)-λ max (X) is incorporated as a penalty term into the processed first optimization problem to obtain the second optimization problem; where X = W*t, X and t are variables, tr(X) represents the trace of X, and λ max (X) represents the largest eigenvalue of X;

[0093] Here, X can be a matrix.

[0094] Step S335: Transform the objective function in the second optimization problem into the form of the difference between two convex functions to obtain the objective convex optimization problem;

[0095] Step S336: Solve the target convex optimization problem using an iterative algorithm to obtain the beamforming weights;

[0096] Step S337: The first signals received by all the relays in the first time slot are weighted and summed using the beamforming weights to obtain the second signal; wherein the first signal is the sum of the useful signal and the interference signal;

[0097] Step S338: Send the second signal to the secondary user in the second time slot.

[0098] Based on the foregoing embodiments, this application further provides a relay communication method based on a spectrum sharing network. The method is applied to relay communication and includes:

[0099] Step S341: Receive the useful signal transmitted by the base station and the interference signal transmitted by the satellite in the first time slot;

[0100] Step S342: When the transmit power of each relay is lower than the maximum transmit power threshold and the signal-to-interference-plus-noise ratio (SIR) of each primary user is higher than the minimum SIR threshold, beamforming design is performed with the goal of maximizing the SIR of the secondary user, resulting in a target optimization problem; wherein the target optimization problem is a non-convex optimization problem.

[0101] Step S343: Add a constraint rank(W) = 1 to the objective optimization problem to transform it into a first optimization problem; where W = ww H w is the weight of the beamforming;

[0102] Step S344: Process the first optimization problem using the Charnes-Cooper transform method, and convert tr(X)-λmax (X) is incorporated as a penalty term into the processed first optimization problem to obtain the second optimization problem; where X = W*t, X and t are variables, tr(X) represents the trace of X, and λ max (X) represents the largest eigenvalue of X;

[0103] Step S345: Transform the objective function in the second optimization problem into the form of the difference between two convex functions to obtain the objective convex optimization problem;

[0104] Step S346: Determine the iterative function based on the target convex optimization problem and the upper limit of the convex function;

[0105] Step S347: Solve the iterative function using a standard optimization toolkit based on the iterative algorithm;

[0106] Step S348: Determine the weights of the beamforming based on the solution when the iterative function satisfies the convergence condition;

[0107] Step S349: The first signals received by all the relays in the first time slot are weighted and summed using the beamforming weights to obtain the second signal; wherein the first signal is the sum of the useful signal and the interference signal;

[0108] Step S350: Send the second signal to the secondary user in the second time slot.

[0109] Based on the foregoing embodiments, this application further provides a relay communication method based on a spectrum sharing network. The method is applied to relay communication and includes:

[0110] Step S351: Receive the useful signal transmitted by the base station and the interference signal transmitted by the satellite in the first time slot;

[0111] Step S352: When the transmit power of each relay is lower than the maximum transmit power threshold and the signal-to-interference-plus-noise ratio of each primary user is higher than the minimum signal-to-interference-plus-noise ratio threshold, beamforming design is performed with the goal of maximizing the signal-to-interference-plus-noise ratio of the secondary user, resulting in a target optimization problem. The target optimization problem is a non-convex optimization problem. The objective function of the target optimization problem is shown in Equation (1), and the constraint conditions of the target optimization problem are shown in Equation (2).

[0112]

[0113] st

[0114] w H Q i w≤P max , i∈{1,2,…,N} (2);

[0115]

[0116] Where N is a natural number greater than or equal to 2, K is a natural number greater than or equal to 1, and F s,u =P s (g s ⊙h u (g) s ⊙h u ) H F t,u =P t (g t ⊙h u (g) t ⊙h u ) H , F t,k =P t (g t ⊙h k (g) t ⊙h k ) H F s,k =P s (g s ⊙h k (g) s ⊙h k ) H , g s =[g s,1 ,g s,2 ,…,g s,N ] T h u =[h 1,u ,h 2,u ,…,h N,u ] T g t =[g t,1 ,g t,2 ,…,g t,N ] T h k =[h 1,k ,h 2,k ,…,h N,k ] T w = [w1 w2…w N ] T P s For the satellite's transmission power, γ th Minimum signal-to-interference-plus-noise ratio threshold for primary users, P t P represents the base station's transmit power. maxLet w be the maximum transmit power threshold for the relay, and g be the beamforming weight vector. t,i Let g be the channel coefficient from the base station to the relay. s,i h is the channel coefficient from satellite to relay. s,k For the satellite-to-primary user channel coefficients, This is the noise power of the relay. For secondary users' noise power, Noise power for primary users, h i,u h is the channel coefficient for relaying to secondary users. i,k This is the channel coefficient for relaying to the primary user.

[0117] Here, ⊙ represents the product. The signal-to-interference-plus-noise ratio (SIR) for secondary users of a secondary network is the ratio of the strength of the received useful signal to the strength of the received interference signal (noise and interference).

[0118] Step S353: Add a constraint rank(W) = 1 to the objective optimization problem to transform it into a first optimization problem; where W = ww H w is the weight of the beamforming;

[0119] Step S354: Process the first optimization problem using the Charnes-Cooper transform method, and convert tr(X)-λ max (X) is incorporated as a penalty term into the processed first optimization problem to obtain the second optimization problem. The objective function of the second optimization problem is shown in formula (3), and the constraint conditions of the second optimization problem are shown in formula (4).

[0120]

[0121] st

[0122]

[0123] Where X = W*t, X and t are variables, ρ≥0 is the penalty factor, tr(·) represents the trace of the matrix, and λ max (·) represents the largest eigenvalue of the matrix.

[0124] Step S355, Define r1(X) = tr(XF) t,u -ρtr(X) and r2(X) = -ρλ max (X), transform the objective function in the second optimization problem into the form of the difference between two convex functions r1(X)-r2(X) to obtain the objective convex optimization problem;

[0125] Step S356: In the target convex optimization problem, r2(X) is... Taking a first-order Taylor expansion, we obtain the upper bound of r²(X): The This is a feasible solution to the objective convex optimization problem;

[0126] Here, a feasible solution refers to any set of decision variable values ​​that satisfies all constraints (both pre- and post-constraints) of a linear programming problem. This set of values ​​is considered a feasible solution to the linear programming problem. In this embodiment, it can be assumed that… This is a feasible solution obtained when i=0. This is an initial value, and we can use the initial value to iterate.

[0127] Step S357: Based on the target convex optimization problem and the upper limit of r2(X), determine the iterative function and the constraint conditions of the iterative function. The iterative function is shown in formula (5), and the constraint conditions of the iterative function are shown in formula (6).

[0128]

[0129] st

[0130]

[0131] Among them, u max (·) represents the eigenvector corresponding to the largest eigenvalue of the matrix. and These are the optimal solutions for the i-th and (i+1)-th iterations, respectively.

[0132] Step S358: Solve the iterative function using a standard optimization toolkit based on the iterative algorithm;

[0133] Here, we input the solution from the first iteration and perform multiple iterations until we reach the point where we can no longer iterate. This is the optimal solution.

[0134] Step S359: Determine the weights of the beamforming based on the solution when the iterative function satisfies the convergence condition;

[0135] Here, when the iterative function satisfies the convergence condition, the beamforming weight vector is: This is the optimal solution for the iterative function, and correspondingly, the beamforming weight vector is also optimal.

[0136] Step S360: The first signals received by all the relays in the first time slot are weighted and summed using the beamforming weights to obtain the second signal; wherein the first signal is the sum of the useful signal and the interference signal;

[0137] Step S361: Send the second signal to the secondary user in the second time slot.

[0138] In recent years, the construction of communication infrastructure has achieved unprecedented progress. Although modern communication networks, mainly composed of wired and wireless communications, have reached a considerable scale, the relative insufficiency of broadband wireless access resources remains one of the bottlenecks hindering the development of informatization. To fundamentally solve this problem, in addition to fully exploring the potential of existing communication resources, it is also necessary to actively explore new communication methods that are highly complementary and compatible with commercially available communication systems and can promptly apply the latest research results. Currently, traditional cellular wireless networks can indeed provide stable and reliable communication quality to a certain extent, but they cannot meet some rapid and urgent communication needs. For example, in communication scenarios such as forest patrol, traffic monitoring, cargo transportation, and temporary battlefields, traditional base station cellular communication networks, due to coverage limitations, cannot effectively address the problem of network coverage changes caused by rapid mobility.

[0139] Against this backdrop, drone communication has emerged, gaining widespread attention in recent years due to its excellent autonomy, flexibility, and hovering capabilities. When public communication networks are paralyzed, drones can provide timely disaster warnings and assist in accelerating rescue and recovery operations. In disaster-stricken areas inaccessible to humans, drones can deliver relief supplies such as medical equipment. In the event of natural disasters such as toxic gas infiltration and wildfires, drones can be used to quickly encircle large areas, thus avoiding the risks associated with human intervention.

[0140] However, most existing technologies only consider the application of single drones in mobile communication networks. Single drones are limited by their limited energy supply, which restricts their flight distance. At the same time, single drone networks are also vulnerable to various network attacks such as Trojans. These factors lead to the disadvantages of single drone communication, such as weak survivability, limited coverage, and unstable links.

[0141] Currently, mainstream communication transmission solutions typically use terrestrial base stations as communication relays. While these solutions achieve relatively ideal results in establishing stable communication links, the cost of building and maintaining terrestrial base stations is high in remote areas with harsh natural environments. Furthermore, they have limitations in terms of flexibility and scalability. For the rapidly developing field of mobile communications, terrestrial base station relays are showing an increasingly significant limiting effect. Utilizing drones as aerial base stations and mobile relays allows users to flexibly and conveniently build communication networks, meeting the deployment needs of various communication scenarios.

[0142] To address the reliability limitations of individual drones, multiple drones can coordinate, collaborate, and self-organize into swarms to create a flight self-organizing network, forming a drone swarm that integrates with other networks to create a collaborative and unified communication system, thereby improving the reliability of drone communication. The collaboration among drone swarms can also extend the survey range of individual drones to some extent, effectively achieving wide-area coverage of the wireless network. Furthermore, from a technical perspective, drones can form virtual multi-antenna arrays through mutual collaboration, and beamforming technology can improve system throughput.

[0143] In some emergency communication scenarios, it is necessary not only to deal with interference from existing communication equipment in the spectrum network, but also to meet the requirement of establishing reliable communication links anytime and anywhere. This poses a challenge to the existing mobile communication architecture. Therefore, there is an urgent need to find a fast and flexible wireless communication networking method to solve the mobility problem in wireless network coverage areas.

[0144] Based on this, this application fully considers the shortcomings existing in actual communication scenarios and proposes a UAV relay cooperative communication method in a spectrum-sharing network. Considering the situation where satellite ground networks and UAV relay networks share spectrum, a distributed beamforming design method is proposed for the multi-UAV relay cooperative communication problem in a spectrum-sharing network. Specifically, under the condition that the signal-to-interference-plus-noise ratio (SIR) of satellite users is higher than a threshold and the transmit power of each UAV is lower than a threshold, the SIR of UAV users is maximized by designing the beamforming weights at the UAV transmitter end. Specifically, the original optimization problem is transformed into a convex optimization problem solvable using a standard convex optimization package through Charnes-Cooper transform, penalty function, and DC method, and an iterative algorithm is proposed to approximate the global optimum. Therefore, the UAV relay cooperative communication method in a spectrum-sharing network provided in this application can effectively solve the multi-UAV cooperative communication problem in a spectrum-sharing network, ensuring communication quality for both satellite users and UAV users while reducing the transmit power of UAVs. Furthermore, the iterative algorithm proposed in this application has low complexity and superior performance.

[0145] To address the problem of UAV relay cooperative communication in spectrum-sharing networks, this application proposes a distributed beamforming method. The aim is to optimize beamforming weights to ensure that the transmit power of each UAV is below P. max And the signal-to-interference-plus-noise ratio of each primary user is higher than γ thUnder the premise of maximizing the signal-to-interference-plus-noise ratio γ of secondary users. In the process of solving the beamforming weight vector, the embodiments of this application use Charnes-Cooper transform, penalty function and DC method to transform the original non-convex optimization problem into a convex optimization problem that can be solved by the standard convex optimization package in Matlab, and propose an iterative algorithm based on DC method to approximate the global optimum.

[0146] Figure 4A This is a schematic diagram of the composition structure of the spectrum sharing network according to an embodiment of this application, as shown below. Figure 4A As shown, the spectrum-sharing network includes a satellite ground network and a drone relay network that shares spectrum with the satellite ground network. The satellite ground network, as the main network, includes K primary users 41 and one satellite 42. The drone relay network, as a secondary network, includes one base station 43, one secondary user 44, and N drone relays 45. The communication process of the drone relay network can be divided into two time slots: In the first time slot, base station 43 sends a signal to drone relay 45, while drone relay 45 receives interference signals from satellite 42. In the second time slot, after weighting by beamforming vector w, drone relay 45 forwards the signal to secondary user 44. Due to the broadcast characteristics of mobile communication, primary user 41 will also be affected by the interference signals emitted by drone relay 45. Therefore, in the second time slot, primary user 41 will receive two types of signals: one is the signal emitted by satellite 42 in the second time slot, and the other is the interference signal emitted by drone relay 45.

[0147] Furthermore, during the communication process of the UAV relay network, it is necessary to determine the value of the beamforming vector w, which can be determined in the following way:

[0148] First, set the satellite's transmission power in the satellite ground network to P. s The minimum signal-to-interference-plus-noise ratio (SIR) threshold for the primary user is γ. th Set the transmit power of the base station in the UAV relay network to P. t And the maximum transmit power threshold for each UAV is P. max .

[0149] Second, the optimization problem is defined as maximizing the signal-to-interference-plus-noise ratio (SINORR) γ of the secondary users, with the constraint that the transmit power of each UAV is below a threshold P. max And the signal-to-interference-plus-noise ratio of the main user is higher than the threshold γ th The optimization variable is the beamforming weight vector w = [w1 w2 … w N ] T .

[0150] In the first time slot, the signal received by the UAV relay is as shown in formula (7), where s tand These are the normalized power signals transmitted by the base station and the satellite, respectively, n i It is Gaussian noise at the drone relay point, g t,i and g s,i These are the channel coefficients from the base station to the drone and from the satellite to the drone, respectively.

[0151]

[0152] In the second time slot, the signal received at the secondary user is as shown in formula (8), and the signal received at the primary user is as shown in formula (9), where h i,u It is the channel coefficient from the drone to the secondary user, n u It is noise at the secondary user location, h i,k and h s,k These are the channel coefficients for drone relay to the primary user and satellite relay to the primary user, respectively. For the signal transmitted by the satellite in the second time slot, n k It's noise at the main user's location.

[0153]

[0154] Here, in formula (18) The useful signal received at the secondary user. Interference signals received at the secondary user level. This refers to the noise signal received at the secondary user.

[0155]

[0156] Here, in formula (19) Useful signals received at the main user's location. The noise signal received at the main user's location. Interference signals received at the main user's location.

[0157] Therefore, the optimization problem can be expressed by formula (10).

[0158]

[0159] Among them, F s,u =P s (g s ⊙h u (g) s ⊙h u ) H F t,u =P t (g t ⊙h u (g) t ⊙hu ) H , F t,k =P t (g t ⊙h k (g) t ⊙h k ) H F s,k =P s (g s ⊙h k (g) s ⊙h k ) H , g s =[g s,1 ,g s,2 ,…,g s,N ] T h u =[h 1,u ,h 2,u ,…,h N,u ] T g t =[g t,1 ,g t,2 ,…,g t,N ] T h k =[h 1,k ,h 2,k ,…,h N,k ] T , For the noise power of the drone, For secondary users' noise power, Noise power for the primary user.

[0160] Third, the optimization problem is transformed, and the transformed optimization problem is solved using the standard convex optimization package. Since the above optimization problem is non-convex and cannot be solved directly, this embodiment transforms the original optimization problem into a convex optimization problem through Charnes-Cooper transformation, penalty function, and DC method.

[0161] Define W = ww H With the constraint rank(W) = 1 added, the original optimization problem can be expressed by formula (11):

[0162]

[0163] Where tr(·) represents the trace of the matrix. Further, using the Charnes-Cooper transformation, two variables t and X are introduced, and W = X / t is set. Then, formula (11) can be transformed into formula (12).

[0164]

[0165] Since the constraint rank(X) = 1 is non-convex, the optimization problem described above is also non-convex. Furthermore, rank(X) = 1 is equivalent to tr(X) - λ. max If (X) = 0, then tr(X) - λ max (X) is incorporated into the objective function as a penalty term, transforming formula (12) into formula (13), where ρ≥0 is the penalty factor, and λ max (·) represents the largest eigenvalue of the matrix.

[0166]

[0167] Where ρ≥0 is the penalty factor, λ max (·) represents the largest eigenvalue of the matrix.

[0168] It should be noted that when tr(X)-λ max When rank(X) approaches 0, not only can the objective function of the optimization problem reach its maximum value, but the constraint rank(X) = 1 can also be satisfied, and the optimization problem at this time is equivalent to the original optimization problem. However, due to λ max Since (X) is not differentiable, the objective function in the above optimization problem remains non-convex. To solve this problem, we can define r1(X) = tr(XF) t,u -ρtr(X), r2(X)=-ρλ max If (X), then the objective function in the above optimization problem can be expressed as r1(X)-r2(X).

[0169] It can be observed that the above equation conforms to the DC structure and can be solved using the DC method. Let... To find a feasible solution to the optimization problem, r2(X) is... The upper bound of r²(X) can be obtained by performing a first-order Taylor expansion. Where u max (·) represents the eigenvector corresponding to the largest eigenvalue of the matrix.

[0170] Fourth, an iterative algorithm is proposed to approximate the optimal beamforming weights. The iterative process of approximating the optimal beamforming weights based on the DC method can be expressed by formula (14):

[0171]

[0172] in, and These are the optimal solutions for the i-th and (i+1)-th iterations, respectively. The above optimization problem satisfies the standard form of a convex optimization problem and can be solved using the standard convex optimization package in Matlab. According to... and Calculate the corresponding γ i and γ i+1 And let i increment, when γ i+1 -γ i ≤10 -3 When the iteration terminates, the optimal beamforming weight vector is:

[0173] Figure 4B This is a simulation diagram of a relay communication method based on a spectrum sharing network according to an embodiment of this application, as shown below. Figure 4B As shown, the satellite transmission power is set to P. s = 2 dBW (decibels per watt), base station transmit power is P t =2dBW, noise power Figure 4B This demonstrates the signal-to-interference-plus-noise ratio (SIR) threshold for the primary user being γ. th This simulation examines the relationship between the transmit power threshold of each UAV and the signal-to-interference-plus-noise ratio (SNR) of secondary users under a 1 dB (decibels) condition. The horizontal axis represents the transmit power threshold of each UAV, and the vertical axis represents the SNR of secondary users. Curve 41 shows the simulation curve for 8 UAVs and 3 primary users; curve 42 shows the simulation curve for 8 UAVs and 4 primary users; curve 43 shows the simulation curve for 4 UAVs and 3 primary users; and curve 44 shows the simulation curve for 4 UAVs and 4 primary users. The simulation curves show that as the transmit power threshold of the UAVs increases, the SNR of the secondary users also increases. Furthermore, it can be seen that the system performance improves with the increase in the number of UAVs, and the system performance also improves with the decrease in the number of primary users.

[0174] This application proposes a relay communication method based on a spectrum-sharing network. The method is based on a satellite terrestrial network and a shared spectrum UAV relay network. The satellite terrestrial network serves as the primary network, containing multiple primary users, while the UAV relay network serves as the secondary network, containing one ground base station, one secondary user, and multiple UAV relays. In the spectrum-sharing network, with the ground base station's transmit power remaining constant, the signal-to-interference-plus-noise ratio (SIR) of the secondary user is maximized by designing the beamforming weights at the UAV end, while ensuring that the transmit power of each UAV is below a threshold and the SIR of the primary user is above a threshold.

[0175] In other words, firstly, we set the minimum signal-to-interference-plus-noise ratio (SIR) threshold for primary users in the satellite-to-ground network, the satellite's transmit power, the base station's transmit power in the UAV relay network, and the maximum transmit power threshold for each UAV. Secondly, we determine that the objective of the optimization problem is to maximize the SIR of secondary users. The constraint is that the transmit power of each drone is below a threshold: stP i ≤P max i∈{1,2,…,N}, and the signal-to-interference-plus-noise ratio (SIR) of the primary user is higher than the threshold: γ k ≤γ th k∈{1,2,…,K}, where P i For the drone's transmit power, γ k The signal-to-interference-plus-noise ratio (SIR) for the primary user is determined. Furthermore, the optimization problem is transformed, and the transformed optimization problem is solved using a standard convex optimization package. An iterative algorithm is proposed to progressively approximate the optimal beamforming weights.

[0176] Compared to single-UAV relay networks, the multi-UAV relay cooperative communication technology proposed in this application can effectively expand the coverage of UAV networks and improve system capacity and spectrum utilization. Furthermore, this application solves the distributed beamforming problem in multi-UAV relay networks, improving the signal-to-interference-plus-noise ratio (SNR) of secondary users while ensuring the communication quality of primary users and limiting UAV transmit power. Moreover, unlike previous constraints that used total relay transmit power, this application considers the limitation of transmit power for each UAV relay, which has strong practical significance. Finally, compared to traditional relay joint optimization methods for interference suppression, the iterative algorithm proposed in this application has low complexity and superior performance.

[0177] Based on the foregoing embodiments, this application provides a relay communication device based on a spectrum sharing network. The device includes various units, modules included in each unit, and components included in each module, which can be implemented by a processor in the relay; of course, it can also be implemented by specific logic circuits. In the implementation process, the processor can be a CPU (Central Processing Unit), MPU (Microprocessor Unit), DSP (Digital Signal Processor), or FPGA (Field Programmable Gate Array), etc.

[0178] The spectrum sharing network includes a main network consisting of satellites, and a secondary network consisting of a base station, a secondary user, and at least two relays. Figure 5This is a schematic diagram of the composition structure of the relay communication device in the embodiments of this application, such as... Figure 5 As shown, the device 500 includes:

[0179] The receiving unit 501 is configured to receive the useful signal transmitted by the base station and the interference signal transmitted by the satellite in the first time slot;

[0180] Weighting unit 502 is used to weight and sum all the first signals received by the relays in the first time slot using predetermined beamforming weights to obtain a second signal; wherein the first signal is the sum of the useful signal and the interference signal;

[0181] The transmitting unit 503 is used to transmit the second signal to the secondary user in the second time slot.

[0182] In some embodiments, the main network further includes at least one main user, and correspondingly, the apparatus further includes:

[0183] The design unit is used to perform beamforming design with the goal of maximizing the signal-to-interference-plus-noise ratio of the secondary user when the transmit power of each relay is lower than the maximum transmit power threshold and the signal-to-interference-plus-noise ratio of each primary user is higher than the minimum signal-to-interference-plus-noise ratio threshold, thereby obtaining the target optimization problem.

[0184] A determining unit is used to determine the weights of the beamforming based on the target optimization problem.

[0185] In some embodiments, the determining unit includes:

[0186] The convex optimization module is used to process the target optimization problem sequentially using the Charnes-Cooper transform method and the convexity difference method to obtain the target convex optimization problem; wherein, the target optimization problem is a non-convex optimization problem;

[0187] The solution module is used to solve the target convex optimization problem using an iterative algorithm to obtain the weights of the beamforming.

[0188] In some embodiments, the convex optimization module includes:

[0189] A semidefinite programming component is used to transform the objective optimization problem into a first semidefinite programming optimization problem.

[0190] The Charnes-Cooper transform component is used to process the first optimization problem using the Charnes-Cooper transform method to obtain the second optimization problem;

[0191] The DC decomposition component is used to transform the objective function in the second optimization problem into the form of the difference between two convex functions, so as to obtain the objective convex optimization problem.

[0192] In some embodiments, the semidefinite programming component includes:

[0193] A semidefinite programming sub-component is used to add a constraint of rank(W) = 1 to the objective optimization problem, thereby transforming the objective optimization problem into the first optimization problem; where W = ww H w is the weight of the beamforming.

[0194] In some embodiments, the Charnes-Cooper conversion component includes:

[0195] The Charnes-Cooper transform subcomponent is used to process the first optimization problem using the Charnes-Cooper transform method and to transform tr(X)-λ max (X) is incorporated as a penalty term into the processed first optimization problem to obtain the second optimization problem;

[0196] Where X = W*t, X and t are variables, tr(X) represents the trace of X, and λ max (X) represents the largest eigenvalue of X.

[0197] In some embodiments, the solving module includes:

[0198] The iterative function determination component is used to determine the iterative function based on the target convex optimization problem and the upper limit of the convex function;

[0199] The solver is used to solve the iterative function based on an iterative algorithm and using a standard optimization toolkit.

[0200] The weight determination component is used to determine the weights of the beamforming based on the solution when the iterative function satisfies the convergence condition.

[0201] The descriptions of the above device embodiments are similar to those of the above method embodiments, and have similar beneficial effects. For technical details not disclosed in the device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0202] It should be noted that, in the embodiments of this application, if the above-described relay communication method based on a spectrum sharing network is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM (Read Only Memory), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0203] Correspondingly, this application provides a relay, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the program, it implements the steps in the relay communication method based on a spectrum sharing network provided in the above embodiments.

[0204] Correspondingly, embodiments of this application provide a readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps in the above-described relay communication method based on a spectrum sharing network.

[0205] It should be noted that the descriptions of the storage medium and device embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0206] It should be noted that, Figure 6 This is a schematic diagram of a hardware entity in an embodiment of this application, such as... Figure 6 As shown, the hardware entity of the relay 600 includes: a processor 601, a communication interface 602, and a memory 603, wherein...

[0207] Processor 601 typically controls the overall operation of relay 600.

[0208] Communication interface 602 enables relay 600 to communicate with other electronic devices or servers over a network.

[0209] The memory 603 is configured to store instructions and applications executable by the processor 601, and can also cache data to be processed or already processed by the processor 601 and the modules in the relay 600 (e.g., image data, audio data, voice communication data and video communication data), which can be implemented by FLASH (flash memory) or RAM (Random Access Memory).

[0210] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0211] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0212] Furthermore, in the various embodiments of this application, all functional units can be integrated into one processing module, or each unit can be a separate unit, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units. Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0213] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0214] The features disclosed in the several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0215] The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0216] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of relaying communication based on a spectrum sharing network, characterized by, The spectrum sharing network comprises a primary network composed of satellites, and a secondary network composed of one base station, one secondary user and at least two relays, and the primary network comprises at least one primary user; the method is applied to the relays, and the method comprises: receiving, in a first time slot, a useful signal transmitted by the base station and an interference signal transmitted by the satellites; when the transmission power of each of the relays is lower than a maximum transmission power threshold, and the signal-to-interference ratio of each of the primary users is higher than a minimum signal-to-interference ratio threshold, performing beamforming design aiming at maximizing the signal-to-interference ratio of the secondary user to obtain a target optimization problem; the variable of the target optimization problem is a weight value of beamforming; determining the weight value of beamforming according to the target optimization problem; performing weighted addition on first signals received by all the relays in the first time slot by using the weight value of beamforming to obtain a second signal; wherein the first signals are sums of the useful signals and the interference signals; transmitting the second signal to the secondary user in a second time slot.

2. The method of claim 1, wherein, The method of determining the weight value of beamforming according to the target optimization problem comprises: sequentially using a Charnes-Cooper transformation method and a convex difference method to process the target optimization problem to obtain a target convex optimization problem; wherein the target optimization problem is a non-convex optimization problem; solving the target convex optimization problem by using an iterative algorithm to obtain the weight value of beamforming.

3. The method of claim 2, wherein, The method of sequentially using the Charnes-Cooper transformation method and the convex difference method to process the target optimization problem to obtain the target convex optimization problem comprises: converting the target optimization problem into a first optimization problem of a semi-definite programming; processing the first optimization problem by using the Charnes-Cooper transformation method to obtain a second optimization problem; changing the target function in the second optimization problem into a difference between two convex functions to obtain the target convex optimization problem.

4. The method of claim 3, wherein, The method of converting the target optimization problem into the first optimization problem of the semi-definite programming comprises: adding a constraint condition of to the target optimization problem to transform the target optimization problem into the first optimization problem, to represent that the constraint condition is non-convex; wherein, , is the weight value of the beamforming.

5. The method of claim 4, wherein, The method of processing the first optimization problem by using the Charnes-Cooper transformation method to obtain the second optimization problem comprises: The first optimization problem is processed by using Charnes-Cooper transformation method, and The second optimization problem is obtained by merging the penalty term into the processed first optimization problem. wherein , and are variables, represents the trace of said , represents the largest eigenvalue of said .

6. The method according to any one of claims 3 to 5, characterized in that, The method of solving the target convex optimization problem by using the iterative algorithm comprises: determining an iterative function according to the target convex optimization problem and an upper limit of the convex function; solving the iterative function by using a standard optimization tool package based on an iterative algorithm; determining the weight value of beamforming according to a solution of the iterative function when the iterative function meets a convergence condition.

7. A relay communication apparatus based on a spectrum sharing network, characterized by, The spectrum sharing network comprises a primary network composed of satellites, and a secondary network composed of one base station, one secondary user and at least two relays; and the primary network comprises at least one primary user; The apparatus comprises: a receiving unit configured to receive, in a first time slot, a useful signal transmitted by the base station and an interference signal transmitted by the satellites; The design unit is configured to, when the transmit power of each of the relays is lower than a maximum transmit power threshold and the signal-to-interference ratio of each of the primary users is higher than a minimum signal-to-interference ratio threshold, perform beamforming design aiming at maximizing the signal-to-interference ratio of the secondary users to obtain a target optimization problem; and the variable of the target optimization problem is a weight value of beamforming. The determination unit is configured to determine the weight value of the beamforming according to the target optimization problem; and the weighting unit is configured to add the first signals received by all the relays in the first time slot by weighting with the weight value of the beamforming to obtain a second signal; wherein the first signal is a sum of the useful signal and the interference signal. The sending unit is configured to send the second signal to the secondary users in a second time slot.

8. A relay comprising a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor implements the steps in the method according to any one of claims 1 to 6 when executing the program.

9. A computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is capable of implementing the steps in the method according to any one of claims 1 to 6 when executed by a processor.

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